Instructions to use danielbubiola/daniel_asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielbubiola/daniel_asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="danielbubiola/daniel_asr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("danielbubiola/daniel_asr") model = AutoModelForCTC.from_pretrained("danielbubiola/daniel_asr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: daniel_asr | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # daniel_asr | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4565 | |
| - Wer: 0.3423 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 1000 | |
| - num_epochs: 30 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 3.4909 | 4.0 | 500 | 1.3485 | 0.8887 | | |
| | 0.5887 | 8.0 | 1000 | 0.4957 | 0.4641 | | |
| | 0.2207 | 12.0 | 1500 | 0.4621 | 0.3971 | | |
| | 0.125 | 16.0 | 2000 | 0.4339 | 0.3756 | | |
| | 0.0829 | 20.0 | 2500 | 0.4618 | 0.3613 | | |
| | 0.0601 | 24.0 | 3000 | 0.4564 | 0.3535 | | |
| | 0.0456 | 28.0 | 3500 | 0.4565 | 0.3423 | | |
| ### Framework versions | |
| - Transformers 4.11.3 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 1.13.3 | |
| - Tokenizers 0.10.3 | |